Is the final model scaling done on the full training set?
The short answer is yes.
When you merge the test set into the train set, you try to squeeze available data till the last drop. The cons and pros of this approach have been considered in other questions in the network 1, 2. But if you decided to go for it, there is no point to not use the whole dataset for the scaling transformation, as the trend &quot;more data leads (generally) to better models&quot; is valid to the scaling to the same extent it's valid for the model itself.